






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]				):
			__a						:  Dict									=     filter(lambda lowerCamelCase_				: p.requires_grad   ,  model.parameters()				)
			__a						:  Union[str, Any]									=     sum([np.prod(p.size()				) for p in model_parameters]				)
			return params


SCREAMING_SNAKE_CASE__				     =			logging.getLogger(__name__)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Optional[Any]				):
			if metric == "rouge2":
						__a						:  int									=     '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						__a						:  Optional[Any]									=     '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						__a						:  Any									=     '{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						__a						:  int									=     '{val_avg_loss:.4f}-{step_count}'
			else:
						raise NotImplementedError(
						    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    ' function.'				)

			__a						:  Union[str, Any]									=     ModelCheckpoint(
			    dirpath=lowerCamelCase_   ,  filename=lowerCamelCase_   ,  monitor=f'''val_{metric}'''   ,  mode='max'   ,  save_top_k=1   ,  every_n_epochs=1   ,  )

			return checkpoint_callback

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Any				):
			return EarlyStopping(
			    monitor=f'''val_{metric}'''   ,  mode='min' if 'loss' in metric else 'max'   ,  patience=lowerCamelCase_   ,  verbose=lowerCamelCase_   ,  )






class 			_UpperCamelCase(       pl.Callback		):

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					__a						:  List[str]									=     {f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
					pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__    )

		@rank_zero_only
		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    pl.Trainer    ,       SCREAMING_SNAKE_CASE__		:    pl.LightningModule    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Any=True    ):



					'''simple docstring'''



					logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
					__a						:  Optional[int]									=     trainer.callback_metrics
					trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}    )
					# Log results
					__a						:  Tuple									=     Path(pl_module.hparams.output_dir    )
					if type_path == "test":
								__a						:  int									=     od / 'test_results.txt'
								__a						:  str									=     od / 'test_generations.txt'
					else:
								# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
								# If people want this it will be easy enough to add back.
								__a						:  Tuple									=     od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
								__a						:  List[str]									=     od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
								results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
								generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
					with open(SCREAMING_SNAKE_CASE__    ,       'a+'    ) as writer:
								for key in sorted(SCREAMING_SNAKE_CASE__    ):
											if key in ["log", "progress_bar", "preds"]:
														continue
											__a						:  Tuple									=     metrics[key]
											if isinstance(SCREAMING_SNAKE_CASE__    ,       torch.Tensor    ):
														__a						:  Union[str, Any]									=     val.item()
											__a						:  List[str]									=     f'''{key}: {val:.6f}\n'''
											writer.write(SCREAMING_SNAKE_CASE__    )

					if not save_generations:
								return

					if "preds" in metrics:
								__a						:  List[Any]									=     '\n'.join(metrics['preds']    )
								generations_file.open('w+'    ).write(SCREAMING_SNAKE_CASE__    )

		@rank_zero_only
		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					try:
								__a						:  Optional[int]									=     pl_module.model.model.num_parameters()
					except AttributeError:
								__a						:  List[Any]									=     pl_module.model.num_parameters()

					__a						:  Dict									=     count_trainable_parameters(SCREAMING_SNAKE_CASE__    )
					# mp stands for million parameters
					trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}    )

		@rank_zero_only
		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    pl.Trainer    ,       SCREAMING_SNAKE_CASE__		:    pl.LightningModule    ):



					'''simple docstring'''



					save_json(pl_module.metrics    ,       pl_module.metrics_save_path    )
					return self._write_logs(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       'test'    )



		@rank_zero_only
		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    pl.Trainer    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					save_json(pl_module.metrics    ,       pl_module.metrics_save_path    )
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")








import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






